Method and system for real time dry low nitrogen oxide (dln) and diffusion combustion monitoring

ABSTRACT

A system and method for monitoring and diagnosing anomalies in a diffusion or dry low NO X  combustion system of a gas turbine, the method including storing a plurality rule sets specific to a temperature spread of the gas turbine exhaust. The method further including determining an anomaly in the performance of the gas turbine using at least one of a swirl angle of the exhaust flow, a health of a plurality of flame detectors of the gas turbine, and a transfer of the gas turbine from a first mode of operation to a second lower NO X  mode of operation, and recommending to an operator of the gas turbine a set of corrective actions to correct the anomaly.

FIELD OF THE INVENTION

This description relates to generally to mechanical/electrical equipmentoperations, monitoring and diagnostics, and more specifically, tosystems and methods for automatically advising operators of anomalousbehavior of machinery.

BACKGROUND OF THE INVENTION

The combustion system is an important item to be monitored in a gasturbine. Traditional combustion monitoring systems use static thresholdsthat do not consider the machine operating conditions, such ascombustion mode and load. As a result, they are inefficient and producefalse or too-late alarms. For example, many hours are currently spentlocating a source of a fault in the case of a real exhaust temperaturespread issue. On the flame detector side, monitoring the digital signalonly or analog output without a correct statistical approach isproblematic and results in false warning.

Traditional monitoring systems suffer from technical deficiencies.Inaccuracy is the most evident, as seen by either too many false alarmsor too late alarms are generally reported, without taking into accountmachine operating conditions; thus, no troubleshooting or littleinformation is provided.

SUMMARY OF THE INVENTION

In one embodiment, a computer-implemented method for monitoring anddiagnosing anomalies in an operation of a gas turbine, the methodimplemented using a computer device coupled to a user interface and amemory device, the method comprising storing a plurality rule sets inthe memory device, the rule sets relative to the operation of the gasturbine, the rule sets including at least one rule expressed as arelational expression of a real-time data output relative to a real-timedata input, the relational expression being specific to at least one ofa temperature spread of an exhaust flow of the gas turbine, a swirlangle of the exhaust flow, a health of a plurality of flame detectors ofthe gas turbine, and a transfer of the gas turbine from a first mode ofoperation to a second lower NO_(X) mode of operation, receivingreal-time and historical data inputs from a condition monitoring systemassociated with the gas turbine, the data inputs relating to parametersaffecting at least one of the temperature spread of the exhaust flow ofthe gas turbine, the swirl angle of the exhaust flow, the health of theplurality of flame detectors of the gas turbine, and the transfer of thegas turbine from the first mode of operation to the second lower NO_(X)mode of operation, determining a fuel gas line pressure drop using thereceived data, comparing the determined pressure drop to a predeterminedthreshold range, and recommending to an operator of the gas turbine totransfer the mode of operation of the gas turbine from the first mode tothe second mode without reducing a load of the gas turbine if thedetermined pressure drop meets the predetermined threshold range.

In another embodiment, a gas turbine monitoring and diagnostic systemfor a gas turbine includes an axial compressor and a low pressureturbine in flow communication, said system comprising a real-time DLNand diffusion combustion rule set, the rule set including a relationalexpression of a real-time data output relative to at least one of thetemperature spread of the exhaust flow of the gas turbine, the swirlangle of the exhaust flow, the health of the plurality of flamedetectors of the gas turbine, and the transfer of the gas turbine fromthe first mode of operation to the second lower NO_(X) mode ofoperation.

In yet another embodiment, one or more non-transitory computer-readablestorage media has computer-executable instructions embodied thereon,wherein when executed by at least one processor, the computer-executableinstructions cause the processor to store a plurality rule sets in thememory device, the rule sets relative to the output of the gas turbine,the rule sets including at least one rule expressed as a relationalexpression of a real-time data output relative to a real-time datainput, the relational expression being specific to at least one of atemperature spread of an exhaust flow of the gas turbine, a swirl angleof the exhaust flow, a health of a plurality of flame detectors of thegas turbine, and a transfer of the gas turbine from a first mode ofoperation to a second lower NO_(X) mode of operation, receive real-timeand historical data inputs from a condition monitoring system associatedwith the gas turbine, the data inputs relating to parameters affectingat least one of the temperature spread of the exhaust flow of the gasturbine, the swirl angle of the exhaust flow, the health of theplurality of flame detectors of the gas turbine, and the transfer of thegas turbine from the first mode of operation to the second lower NO_(X)mode of operation, receive a plurality of temperature outputs from oneor more temperature sensors associated with the flow of gas turbineexhaust, and determine a temperature spread of the flow of gas turbineexhaust using the received plurality of temperature outputs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1-10 show exemplary embodiments of the method and system describedherein.

FIG. 1 is a schematic block diagram of a remote monitoring anddiagnostic system in accordance with an exemplary embodiment of thepresent invention.

FIG. 2 is a block diagram of an exemplary embodiment of a networkarchitecture of a local industrial plant monitoring and diagnosticsystem, such as a distributed control system (DCS).

FIG. 3 is a block diagram of an exemplary rule set that may be used withLMDS shown in FIG. 1.

FIG. 4 is a side elevation view of a gas turbine engine in accordancewith an exemplary embodiment of the present disclosure.

FIG. 5 is a schematic representation of the placement of twelvethermocouples spaced approximately evenly about diffuser in accordancewith an exemplary embodiment of the present disclosure.

FIG. 6 is a graph illustrating a correlation between burner clogging andexhaust temperature spread.

FIG. 7 is a schematic block diagram of a flame detector (FD) circuitthat may be used with gas turbine engine shown in FIG. 4 in accordancewith an exemplary embodiment of the present disclosure.

FIG. 8 is a screen capture of a trace of flame detector circuit analogoutputs and digital output.

FIG. 9 is a flow diagram of operation of gas turbine engine during aloading and an unloading process.

FIG. 10 is a schematic piping diagram of a portion of a fuel system 1000that may be used with gas turbine engine shown in FIG. 4 in accordancewith an exemplary embodiment of the present disclosure.

Although specific features of various embodiments may be shown in somedrawings and not in others, this is for convenience only. Any feature ofany drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description illustrates embodiments of theinvention by way of example and not by way of limitation. It iscontemplated that the invention has general application to analyticaland methodical embodiments of monitoring equipment operation inindustrial, commercial, and residential applications.

The combustion system is an important item to be monitored in a gasturbine. Dry Low NOx (DLN) systems are more complicated and involvedifferent combustion modes than traditional gas turbines. As usedherein, NOx refers to mono-nitrogen oxides, NO and NO2 (nitric oxide andnitrogen dioxide). The real-time DLN and diffusion combustion rule setfacilitates preventing incorrect combustion operation, identifyingdirect guidelines for troubleshooting, and warns against early signs offailure, giving gas turbine operators time to act and/or schedule shutdowns.

The real-time DLN and diffusion combustion rule set includes thefollowing combustion rules as part of an online monitoring system:

1. Exhaust temperature spread as a function of combustion mode and load:For a DLN system, specifying one constant threshold for the exhausttemperature spread will lead to either false alarms or too late alarms.There is a transition sequence during which the combustion transfersfrom one mode to another; for example: Primary, Lean-Lean, Secondary,Premix or Extended Lean-Lean. During each mode, a proper threshold forthe spread is identified and when loading the machine in Lean-Lean mode,the spread is specified as a function of the firing temperature. Fordiffusion combustion the spread is compared with a threshold which is,for example, approximately 0.7 the allowable spread. A rule to highlightexhaust temperature thermocouple sensor failure is also defined.

Exhaust spread thresholds are set more accurately, because they are setfor each combustion mode and as a function of load. The exhaust spreadalarms are validated to ensure a real issue is the cause. The real-timeDLN and diffusion combustion rule set facilitates and enhancestroubleshooting: for example, the swirl angle calculator locates thesource of fault (combustor(s) and/or fuel nozzles) and reducestroubleshooting time. On diffusion combustion gas turbine, a rule isalso provided that determines whether a high spread is caused by afaulty sensor, so that the troubleshooting process can immediatelyproceed towards the right root cause identification.

2. The real-time DLN and diffusion combustion rule set also performs aswirl angle calculation to trace back the spread to a source or a faultycombustor(s), which will significantly reduce troubleshooting time. Whena spread is detected on a multi-can gas turbine, it is notstraightforward to judge on the source of the problem (faultycombustor), because the thermocouples are not placed adjacent to thecombustor cans. The rule set traces back from the spread anomaly at theexhaust diffuser to the faulty combustor. A correlation used in the ruleengine identifies the faulty combustor in the event of a real spread.

3. Flame detector health is important too, as flame detector degradationover time and other problems can lead to multiple trips with allassociated costs and loss of production. The real time DLN and diffusioncombustion rule set includes an algorithm that analyzes the health offlame detectors and generates warnings and recommendations related tothis real time analysis, which facilitates performance of goodmaintenance of the flame detector system to avoid false loss-of-flamealarms and trips. The raw pulse signal coming from the flame sensor (UVsensor) is processed by the control system in two different ways; asanalog output and digital output. Digital signals are used to detect aflame and are involved in the control panel logics, while analog signalsare not used. Field testing and several tests have shown a high degreeof variability and low repeatability of flame detector signals, whichare the cause of “false” loss-of-flame on secondary and trips, runningin Premix mode. The health of the flame detector depends on many factorsincluding air humidity, dirt accumulate on the lens and electric wireconnections. In the real-time DLN and diffusion combustion rule set, theanalog output is used to monitor the secondary flame detectors: eachsignal is processed using a statistical approach to identify the noiseand variation and generate a “health count metric”. This metric is usedto define thresholds and indicate if it is needed to change or tune thesensors. The output recommendation is to either replace, tune, check, orclean the lens of the detector. The flame detector rule of the real-timeDLN and diffusion combustion rule set monitors degradation over timeand, thus, can predict early signs of failure. The outputrecommendations can distinguish a deteriorating detector from a dirty orfoggy one.

4. Unnecessary unloading and excess flaring is currently needed totransfer from the Extended Lean-Lean (EXT-LL) mode to Premix mode.Hence, any benefits associated with low emissions are contradicted bythis excess flare. Based on a fuel gas line pressure drop calculation,the real-time DLN and diffusion combustion rule set evaluates thepossibility of transferring directly without unloading, which can reduceflaring and allowing the transfer without reducing gas turbine load. TheDLN transfer rule allows operators to understand the possibility ofavoiding unnecessary unloading to save time, fuel and emissionsresulting from excess process gas flaring.

FIG. 1 is a schematic block diagram of remote monitoring and diagnosticsystem 100 in accordance with an exemplary embodiment of the presentinvention. In the exemplary embodiment, system 100 includes a remotemonitoring and diagnostic center 102. Remote monitoring and diagnosticcenter 102 is operated by an entity, such as, an OEM of a plurality ofequipment purchased and operated by a separate business entity, such as,an operating entity. In the exemplary embodiment, the OEM and operatingentity enter into a support arrangement whereby the OEM providesservices related to the purchased equipment to the operating entity. Theoperating entity may own and operate purchased equipment at a singlesite or multiple sites. Moreover, the OEM may enter into supportarrangements with a plurality of operating entities, each operatingtheir own single site or multiple sites. The multiple sites each maycontain identical individual equipment or pluralities of identical setsof equipment, such as trains of equipment. Additionally, at least someof the equipment may be unique to a site or unique to all sites.

In the exemplary embodiment, a first site 104 includes one or moreprocess analyzers 106, equipment monitoring systems 108, equipment localcontrol centers 110, and/or monitoring and alarm panels 112 eachconfigured to interface with respective equipment sensors and controlequipment to effect control and operation of the respective equipment.The one or more process analyzers 106, equipment monitoring systems 108,equipment local control centers 110, and/or monitoring and alarm panels112 are communicatively coupled to an intelligent monitoring anddiagnostic system 114 through a network 116. Intelligent monitoring anddiagnostic (IMAD) system 114 is further configured to communicate withother on-site systems (not shown in FIG. 1) and offsite systems, suchas, but not limited to, remote monitoring and diagnostic center 102. Invarious embodiments, IMAD 114 is configured to communicate with remotemonitoring and diagnostic center 102 using for example, a dedicatednetwork 118, a wireless link 120, and the Internet 122.

Each of a plurality of other sites, for example, a second site 124 andan nth site 126 may be substantially similar to first site 104 althoughmay or may not be exactly similar to first site 104.

FIG. 2 is a block diagram of an exemplary embodiment of a networkarchitecture 200 of a local industrial plant monitoring and diagnosticsystem, such as a distributed control system (DCS) 201. The industrialplant may include a plurality of plant equipment, such as gas turbines,centrifugal compressors, gearboxes, generators, pumps, motors, fans, andprocess monitoring sensors that are coupled in flow communicationthrough interconnecting piping, and coupled in signal communication withDCS 201 through one or more remote input/output (I/O) modules andinterconnecting cabling and/or wireless communication. In the exemplaryembodiment, the industrial plant includes DCS 201 including a networkbackbone 203. Network backbone 203 may be a hardwired data communicationpath fabricated from twisted pair cable, shielded coaxial cable or fiberoptic cable, for example, or may be at least partially wireless. DCS 201may also include a processor 205 that is communicatively coupled to theplant equipment, located at the industrial plant site or at remotelocations, through network backbone 203. It is to be understood that anynumber of machines may be operatively connected to network backbone 203.A portion of the machines may be hardwired to network backbone 203, andanother portion of the machines may be wirelessly coupled to backbone203 via a wireless base station 207 that is communicatively coupled toDCS 201. Wireless base station 207 may be used to expand the effectivecommunication range of DCS 201, such as with equipment or sensorslocated remotely from the industrial plant but, still interconnected toone or more systems within the industrial plant.

DCS 201 may be configured to receive and display operational parametersassociated with a plurality of equipment, and to generate automaticcontrol signals and receive manual control inputs for controlling theoperation of the equipment of industrial plant. In the exemplaryembodiment, DCS 201 may include a software code segment configured tocontrol processor 205 to analyze data received at DCS 201 that allowsfor on-line monitoring and diagnosis of the industrial plant machines.Data may be collected from each machine, including gas turbines,centrifugal compressors, pumps and motors, associated process sensors,and local environmental sensors including, for example, vibration,seismic, temperature, pressure, current, voltage, ambient temperatureand ambient humidity sensors. The data may be pre-processed by a localdiagnostic module or a remote input/output module, or may transmitted toDCS 201 in raw form.

A local monitoring and diagnostic system (LMDS) 213 may be a separateadd-on hardware device, such as, for example, a personal computer (PC),that communicates with DCS 201 and other control systems 209 and datasources through network backbone 203. LMDS 213 may also be embodied in asoftware program segment executing on DCS 201 and/or one or more of theother control systems 209. Accordingly, LMDS 213 may operate in adistributed manner, such that a portion of the software program segmentexecutes on several processors concurrently. As such, LMDS 213 may befully integrated into the operation of DCS 201 and other control systems209. LMDS 213 analyzes data received by DCS 201, data sources, and othercontrol systems 209 to determine an operational health of the machinesand/or a process employing the machines using a global view of theindustrial plant.

In the exemplary embodiment, network architecture 100 includes a servergrade computer 202 and one or more client systems 203. Server gradecomputer 202 further includes a database server 206, an applicationserver 208, a web server 210, a fax server 212, a directory server 214,and a mail server 216. Each of servers 206, 208, 210, 212, 214, and 216may be embodied in software executing on server grade computer 202, orany combinations of servers 206, 208, 210, 212, 214, and 216 may beembodied alone or in combination on separate server grade computerscoupled in a local area network (LAN) (not shown). A data storage unit220 is coupled to server grade computer 202. In addition, a workstation222, such as a system administrator's workstation, a user workstation,and/or a supervisor's workstation are coupled to network backbone 203.Alternatively, workstations 222 are coupled to network backbone 203using an Internet link 226 or are connected through a wirelessconnection, such as, through wireless base station 207.

Each workstation 222 may be a personal computer having a web browser.Although the functions performed at the workstations typically areillustrated as being performed at respective workstations 222, suchfunctions can be performed at one of many personal computers coupled tonetwork backbone 203. Workstations 222 are described as being associatedwith separate exemplary functions only to facilitate an understanding ofthe different types of functions that can be performed by individualshaving access to network backbone 203.

Server grade computer 202 is configured to be communicatively coupled tovarious individuals, including employees 228 and to third parties, e.g.,service providers 230. The communication in the exemplary embodiment isillustrated as being performed using the Internet, however, any otherwide area network (WAN) type communication can be utilized in otherembodiments, i.e., the systems and processes are not limited to beingpracticed using the Internet.

In the exemplary embodiment, any authorized individual having aworkstation 232 can access LMDS 213. At least one of the client systemsmay include a manager workstation 234 located at a remote location.Workstations 222 may be embodied on personal computers having a webbrowser. Also, workstations 222 are configured to communicate withserver grade computer 202. Furthermore, fax server 212 communicates withremotely located client systems, including a client system 236 using atelephone link (not shown). Fax server 212 is configured to communicatewith other client systems 228, 230, and 234, as well.

Computerized modeling and analysis tools of LMDS 213, as described belowin more detail, may be stored in server 202 and can be accessed by arequester at any one of client systems 204. In one embodiment, clientsystems 204 are computers including a web browser, such that servergrade computer 202 is accessible to client systems 204 using theInternet. Client systems 204 are interconnected to the Internet throughmany interfaces including a network, such as a local area network (LAN)or a wide area network (WAN), dial-in-connections, cable modems andspecial high-speed ISDN lines. Client systems 204 could be any devicecapable of interconnecting to the Internet including a web-based phone,personal digital assistant (PDA), or other web-based connectableequipment. Database server 206 is connected to a database 240 containinginformation about industrial plant 10, as described below in greaterdetail. In one embodiment, centralized database 240 is stored on servergrade computer 202 and can be accessed by potential users at one ofclient systems 204 by logging onto server grade computer 202 through oneof client systems 204. In an alternative embodiment, database 240 isstored remotely from server grade computer 202 and may benon-centralized.

Other industrial plant systems may provide data that is accessible toserver grade computer 202 and/or client systems 204 through independentconnections to network backbone 204. An interactive electronic techmanual server 242 services requests for machine data relating to aconfiguration of each machine. Such data may include operationalcapabilities, such as pump curves, motor horsepower rating, insulationclass, and frame size, design parameters, such as dimensions, number ofrotor bars or impeller blades, and machinery maintenance history, suchas field alterations to the machine, as-found and as-left alignmentmeasurements, and repairs implemented on the machine that do not returnthe machine to its original design condition.

A portable vibration monitor 244 may be intermittently coupled to LANdirectly or through a computer input port such as ports included inworkstations 222 or client systems 204. Typically, vibration data iscollected in a route, collecting data from a predetermined list ofmachines on a periodic basis, for example, monthly or other periodicity.Vibration data may also be collected in conjunction withtroubleshooting, maintenance, and commissioning activities. Further,vibration data may be collected continuously in a real-time or nearreal-time basis. Such data may provide a new baseline for algorithms ofLMDS 213. Process data may similarly, be collected on a route basis orduring troubleshooting, maintenance, and commissioning activities.Moreover, some process data may be collected continuously in a real-timeor near real-time basis. Certain process parameters may not bepermanently instrumented and a portable process data collector 245 maybe used to collect process parameter data that can be downloaded to DCS201 through workstation 222 so that it is accessible to LMDS 213. Otherprocess parameter data, such as process fluid composition analyzers andpollution emission analyzers may be provided to DCS 201 through aplurality of on-line monitors 246.

Electrical power supplied to various machines or generated by generatedby generators with the industrial plant may be monitored by a motorprotection relay 248 associated with each machine. Typically, suchrelays 248 are located remotely from the monitored equipment in a motorcontrol center (MCC) or in switchgear 250 supplying the machine. Inaddition, to protection relays 248, switchgear 250 may also include asupervisory control and data acquisition system (SCADA) that providesLMDS 213 with power supply or power delivery system (not shown)equipment located at the industrial plant, for example, in a switchyard,or remote transmission line breakers and line parameters.

FIG. 3 is a block diagram of an exemplary rule set 280 that may be usedwith LMDS 213 (shown in FIG. 1). Rule set 280 may be a combination ofone or more custom rules, and a series of properties that define thebehavior and state of the custom rules. The rules and properties may bebundled and stored in a format of an XML string, which may be encryptedbased on a 25 character alphanumeric key when stored to a file. Rule set280 is a modular knowledge cell that includes one or more inputs 282 andone or more outputs 284. Inputs 282 may be software ports that directdata from specific locations in LMDS 213 to rule set 280. For example,an input from a pump outboard vibration sensor may be transmitted to ahardware input termination in DCS 201. DCS 201 may sample the signal atthat termination to receive the signal thereon. The signal may then beprocessed and stored at a location in a memory accessible and/orintegral to DCS 201. A first input 286 of rule set 280 may be mapped tothe location in memory such that the contents of the location in memoryis available to rule set 280 as an input. Similarly, an output 288 maybe mapped to another location in the memory accessible to DCS 201 or toanother memory such that the location in memory contains the output 288of rule set 280.

In the exemplary embodiment, rule set 280 includes one or more rulesrelating to monitoring and diagnosis of specific problems associatedwith equipment operating in an industrial plant, such as, for example, agas reinjection plant, a liquid natural gas (LNG) plant, a power plant,a refinery, and a chemical processing facility. Although rule set 280 isdescribed in terms of being used with an industrial plant, rule set 280may be appropriately constructed to capture any knowledge and be usedfor determining solutions in any field. For example, rule set 280 maycontain knowledge pertaining to economic behavior, financial activity,weather phenomenon, and design processes. Rule set 280 may then be usedto determine solutions to problems in these fields. Rule set 280includes knowledge from one or many sources, such that the knowledge istransmitted to any system where rule set 280 is applied. Knowledge iscaptured in the form of rules that relate outputs 284 to inputs 282 suchthat a specification of inputs 282 and outputs 284 allows rule set 280to be applied to LMDS 213. Rule set 280 may include only rules specificto a specific plant asset and may be directed to only one possibleproblem associated with that specific plant asset. For example, rule set280 may include only rules that are applicable to a motor or amotor/pump combination. Rule set 280 may only include rules thatdetermine a health of the motor/pump combination using vibration data.Rule set 280 may also include rules that determine the health of themotor/pump combination using a suite of diagnostic tools that include,in addition to vibration analysis techniques, but may also include, forexample, performance calculational tools and/or financial calculationaltools for the motor/pump combination.

In operation, rule set 280 is created in a software developmental toolthat prompts a user for relationships between inputs 282 and outputs284. Inputs 282 may receive data representing, for example digitalsignals, analog signals, waveforms, processed signals, manually enteredand/or configuration parameters, and outputs from other rule sets. Ruleswithin rule set 280 may include logical rules, numerical algorithms,application of waveform and signal processing techniques, expert systemand artificial intelligence algorithms, statistical tools, and any otherexpression that may relate outputs 284 to inputs 282. Outputs 284 may bemapped to respective locations in the memory that are reserved andconfigured to receive each output 284. LMDS 213 and DCS 201 may then usethe locations in memory to accomplish any monitoring and/or controlfunctions LMDS 213 and DCS 201 may be programmed to perform. The rulesof rule set 280 operate independently of LMDS 213 and DCS 201, althoughinputs 282 may be supplied to rule set 280 and outputs 284 may besupplied to rule set 280, directly or indirectly through interveningdevices.

During creation of rule set 280, a human expert in the field divulgesknowledge of the field particular to a specific asset using adevelopment tool by programming one or more rules. The rules are createdby generating expressions of relationship between outputs 284 and inputs282. Operands may be selected from a library of operands, usinggraphical methods, for example, using drag and drop on a graphical userinterface built into the development tool. A graphical representation ofan operand may be selected from a library portion of a screen display(not shown) and dragged and dropped into a rule creation portion.Relationships between input 282 and operands are arranged in a logicaldisplay fashion and the user is prompted for values, such as, constants,when appropriate based on specific operands and specific ones of inputs282 that are selected. As many rules that are needed to capture theknowledge of the expert are created. Accordingly, rule set 280 mayinclude a robust set of diagnostic and/or monitoring rules or arelatively less robust set of diagnostic and/or monitoring rules basedon a customer's requirements and a state of the art in the particularfield of rule set 280. The development tool provides resources fortesting rule set 280 during the development to ensure variouscombinations and values of inputs 282 produce expected outputs atoutputs 284.

As described below, rule sets are defined to assess exhaust temperaturespread as a function of combustion mode and load, a swirl anglecalculation to trace back the exhaust temperature spread to a source ora faulty combustor(s), the health of flame detectors and generateswarnings and recommendations to avoid false loss-of-flame alarms andtrips, unnecessary unloading and excess flaring currently needed totransfer from the Extended Lean-Lean (EXT-LL) mode to Premix mode of gasturbine operation.

FIG. 4 is a side elevation view of a gas turbine engine 400 inaccordance with an exemplary embodiment of the present disclosure. Inthe exemplary embodiment, gas turbine engine 400 includes a plurality ofpartialized combustion chambers 402 positioned in flow communicationwith a downstream low pressure or load turbine 404, and a diffuser 406positioned downstream of low pressure turbine 404. Diffuser 406 includesa plurality of thermocouples 408 positioned about an interior ofdiffuser 406 in a flowpath of exhaust gases exiting low-pressure turbine404. In the exemplary embodiment, thermocouples 408 number thirteen,which are evenly spaced circumferentially about diffuser 406. In variousembodiments, other numbers of thermocouples 408 are used, which may bespaced as is convenient in diffuser 406.

In the exemplary embodiment, thermocouples 408 are communicativelycoupled to high spread detector 410, which is configured to receivetemperature signals and to apply one or more exhaust spread detectionrule sets to the signals. The partialized combustion chambers 402 arespaced circumferentially about gas turbine engine 400. The exhaust gasesexiting each combustion chamber 402 vary in temperature based oncombustion conditions within each combustion chamber 402. The exhaustgases of each combustion chamber 402 tend to mix only somewhat with theexhaust gases exiting others of the plurality of combustion chambers402. Depending on the gas turbine engine operating conditions, includingbut not limited to load, airflow, and combustion chamber 402 operatingcondition, each thermocouple 408 may be closely associated with adiscernible one or more of combustion chambers 402. Such closeassociation permits a detection of a problem with a burner in one ofcombustion chambers 402 by detecting anomalies in the temperature spreadin diffuser 406 as sensed by thermocouples 408.

An exhaust spread rule set associated with high spread detector 410evaluates swirl angle, which, as used herein, refers to the anglebetween the measured representative exhaust gas temperature, at varyingloads, and the combustion chamber 402 source-location. In the exemplaryembodiment, the exhaust spread rule set is a transfer function with thefollowing inputs:

Exhaust temperature thermocouples readings (TTXD_(—)1, . . .TTXD_(—)13*)

Exhaust temperature spread (TTXSP1*)

High pressure turbine speed—percentage (TNH*)

Low pressure turbine speed—percentage (TNL*)

Absolute Pressure compressor discharge (PCD_abs*)

Ambient pressure (AFPAP*)

The exhaust spread rule set is configured to output a swirl angle and acold/hot spots evaluation. The output is used to identify a location ofa probable cause of temperature spread around diffuser 406. The exhaustspread rule set is configured to output steps to be performed fortroubleshooting when a swirl angle that exceeds a predeterminedthreshold range or when another indicator of temperature spread anomalyis detected. For example, the exhaust spread rule set may outputtroubleshooting steps that include for example, 1. Correctly identifythe hot and cold spots in the exhaust temperature profile, 2. Trace theexhaust temperature anomaly through the gas swirl angle to a particularcombustion chamber location, 3. Identify hardware which is capable ofproducing a variation in the combustion pattern.

The applied methodology of the exhaust spread rule set includesevaluating the presence of a cold/hot spot, locating the cold/hotregion, selecting the coldest/hottest thermocouples and itscorresponding location in the exhaust plenum, perform a check ofadjacent thermocouples, calculating the swirl angle using the exhaustspread rule set transfer function, from the location of the lowthermocouple, back-trace the amount of the swirl angle to identify thelocation of the probable cause.

FIG. 5 is a schematic representation of the placement of twelvethermocouples 408 spaced approximately evenly about diffuser 406 inaccordance with an exemplary embodiment of the present disclosure. Aflow of exhaust gases through diffuser 406 would be oriented into or outof the page on FIG. 5. Based on each thermocouples 408 fixed position indiffuser 406 a relationship between the temperatures sensed by each ofthermocouples 408 and associated combustion chambers 402 may bedetermined and monitored. An uncertainty band 500 may be used todescribe a relative uncertainty of the determined swirl angle. Suchuncertainty may be affected by for example, load on gas turbine engine400.

FIG. 6 is a graph 550 illustrating a correlation between burner cloggingand exhaust temperature spread. Graph 550 include a an x-axis 552graduated in units of % burner clogging and a y-axis 554 graduated inunits of temperature of the exhaust spread. A trace 556 is a curve-fitover several data points from field analysis illustrating thecorrelation between burner clogging and exhaust temperature spread.

The temperature spread at the exit of the combustion chambers 402 is afunction of for example, but not limited to the combustion mode of gasturbine engine 400, a fuel split, and a power output of gas turbineengine 400. The DLN-1 combustion monitoring rule set is a simple rulebased on a predetermined threshold range.

The DLN-1 combustion monitoring rule set receives as inputs:

Combustion mode (DLN_MODE_GAS*)

Average exhaust temperature (TTXM*)

Exhaust temperature spread (TTXSP1*)

Exhaust temperature spread limit (TTXSPL*)

Combustion reference temperature (CTF*)

Exhaust temperature thermocouples readings (TTXD_(—)1, . . .TTXD_(—)13*)

The threshold used to signal a monitoring anomaly depends primarily onthe combustion mode and gas turbine engine load. For example:

Warm-Up: 60° F.

Primary Mode: 45° F.

Lean-Lean Mode: (TTXM-CTF)*0.075+30° F.

Premix-Steady State Mode: 75° F.

Extended-Lean Lean Mode load: 80° F.

The DLN-1 combustion monitoring rule set outputs alarms, indications,such as, but not limited to, check for broken thermocouple or check forplugged burners. The DLN-1 combustion monitoring rule set also outputssteps for troubleshooting, for example:

1. Correctly identify the hot and cold spots in the exhaust temperatureprofile

2. Trace the exhaust temperature anomaly through a known threshold

3. Investigate primary and secondary burner involvement

The applied methodology of the DLN-1 combustion monitoring rule setincludes locating the cold region by analyzing the exhaust temperaturedata, selecting the coldest/hottest thermocouples and its correspondinglocation in the exhaust plenum, evaluating the presence of a cold/hotspot, detect any sudden spread increase higher than 25° F., calculating(S1) Spread#1 (TTXSP1)=hottest−coldest thermocouple temperature, (S2)Spread#2 (TTXSP2)=hottest−2nd coldest thermocouple temperature, checkingadjacent thermocouple for consistency, recording spreads in relevantconditions (Primary HL, Secondary, . . . ), defining threshold fromDLN-1 Combustor good practice, and comparing both spreads with the giventhreshold.

FIG. 7 is a schematic block diagram of a flame detector (FD) circuit 600that may be used with gas turbine engine 400 (shown in FIG. 4) inaccordance with an exemplary embodiment of the present disclosure. Inthe exemplary embodiment, flame detector circuit 600 may be used with aflame detection rule set to provide an indication of the health,sensitivity, and operability of the flame detectors (not shown), whichleads to a reduced occurrence of trip due to instrumentation failure.The rule set associated with a secondary FDs sensitivity check is asimple rule set based on values for monitored parameters being within apredetermined threshold.

The inputs to the FD rule set include:

FDs analog signals (fd_intens_(—)1, . . . fd_intens_(—)8)

FDs logical signals (L28FDA, . . . L28FDH)

Relative humidity signal (CMHUM)

The output of the FD rule set includes alarms, such as, but not limitedto “Flame detectors changing” and “Flame detector to be tuned.”

In the exemplary embodiment, a raw pulse signal from a flame sensor isprocessed by the FD rule set in two different ways, the analog outputs(FD_INTENS_n) 602 are frequency outputs generated by using a fixed timewindow of one second for monitoring purposes. The digital output(L28FDn) 604 is generated by comparing a frequency output based on adifferent time window, for example, 1/16 second with the correspondingcount thresholds set-up in the control system's interfacesflame-on/flame-off logic.

FIG. 8 is a screen capture 700 of a trace of analog outputs 602 anddigital output 604. Detection levels, and detection time are the controlparameters used for FD threshold tuning. The frequency threshold levelis calculated and defined by:

Detection level=14, (frequency threshold=87.5 Hz), digital signal isflat and equal to 1.

Detection level=16, (frequency threshold=100 Hz), digital signal beginsto flicker, switching from 0 to 1.

Detection level=18, (frequency threshold=112.5 Hz), digital signalflickering.

Detection level=20, (frequency threshold=120 Hz), residual spike ofL28fdf

Detection level=22, (frequency level=137.5 Hz), digital signal is flatand equal to 0.

From analysis performed on several field data, for each secondary flamesensor the following condition is used:

If: (Avg−7*STDV_(calculated))*detection time ( 1/16 s)<1—the flamedetector will be replaced.

If: (Avg−7*STDV_(calculated))*detection time ( 1/16 s)<2—the flamedetector will be tuned.

FIG. 9 is a flow diagram 900 of operation of gas turbine engine 400during a loading and an unloading process. An axis 902 indicates GT loadfor the loading operating area 904 and unloading operating area 906.Arrows indicate a path gas turbine engine 400 may take in traversing theoperating areas. A direct transfer rule set is used to calculate thepossibility of transferring directly from EXT-LL mode of operationdirectly into the PREMIX mode of operation.

In the exemplary embodiment, direct transfer rule set is a transferfunction type rule set. Direct transfer rule set receives as inputs:

Fuel gas pressure upstream SVR

Intervalve pressure (FPG2*)

Compressor discharge pressure (PCD*)

Ambient pressure (AFPAP*)

Fuel gas temperature (FGT2*)

Gas control valve (GCV), Stop-Ratio Valve (SRV), Gas control valve (GCV)characterization—kv and Xt

Secondary burner effective area

Direct transfer rule set outputs:

Pressure downstream GCV

Fuel gas flow estimation

Indication of unit capability to transfer directly from EXT-LL intoPREMIX

DLN-1 operation, from start-up to full load, involves five differentmodes of combustion in the multi-zone combustion liner. The distributionof the fuel and flame to the different zones is matched to turbine speedand load conditions to obtain the best performance and emissions withstable flames operation.

If the unit is running in EXTENDED LEAN-LEAN, with the Current DLN-1logic in order to get PREMIX STEADY STATE it is necessary to:

Unload the unit below ˜40% Base Load*, transferring back into LEAN-LEANPOSITIVE.

Transfer into PREMIX STEADY-STATE by increasing load.

Moreover the ignition transformer protection logic introduces anotherlimitation inhibiting PREMIX transfer-in, if the transformer duty cycleis exceeded.

FIG. 10 is a schematic piping diagram of a portion of a fuel system 1000that may be used with gas turbine engine 400 (shown in FIG. 4) inaccordance with an exemplary embodiment of the present disclosure.

The DLN-1 capability of transfer into PREMIX is related to the abilityof maintaining choking condition on a GCV valve 1002 during SECONDARYtransfer mode.

GCV upstream pressure 1004 and SRV upstream pressure 1006 are defined inorder to feed all the amount of gas into a “Transferless” secondary fuelnozzle 1008, without drops in unit load during SECONDARY transfer mode.

The condition for having a good transfer into PREMIX mode can becalculated in real time in order to identify an enlarged window forPREMIX availability including a direct transfer from EXT-LL to PREMIX.

Direct transfer EXT-LL PREMIX—rule development includes

1st Step—Fuel mass flow calculation.

Assuming the gas control valve (GCV) choked and N=1:

  ? = (?_(?))? = 1.23$\mspace{20mu} {{M = {\text{?}\text{?}\text{?}\sqrt{\text{?}^{\text{?}}\left( \text{?} \right)\text{?}}}},{\text{?}\text{indicates text missing or illegible when filed}}}$

where

k=cp/cv is the one of the leanest gas from job CSO

R is the one of the leanest gas from fuel job CSO

A_(ev)=effective area as a function of stroke (from table orcorrelations)

2^(nd) STEP—Primary fuel nozzle pressure [P8] 1010 calculation, whenonly secondary nozzle is fed.

P_(CC)=PCD (1−PLF)—with PLF ˜4%

$\mspace{20mu} {{{\text{?} \cdot \sqrt{\left( {\text{?},\left\lbrack {1 - \left( \text{?} \right\rbrack} \right.} \right.}} = {\text{?}\sqrt{\text{?}}}},{\text{?}\text{indicates text missing or illegible when filed}}}$

where:

T8=FGT fuel gas temperature

R is the one of the leanest gas

Aeff=effective area as a function of pressure ratio across the burner

k=cp/cv is the one of the leanest gas from job CSO

R is the one of the leanest gas of the leanest gas from job CSO

3^(rd) STEP—GCV downstream 1012 calculation, when only secondary nozzleis fed and P7˜P8.

$\mspace{20mu} {\text{?} = {\text{?}\left\{ {{\begin{matrix}{\text{?}} \\\text{?}\end{matrix}\mspace{20mu} \text{?}} = {\frac{k}{1.4}\text{?}\text{indicates text missing or illegible when filed}}} \right.}}$

Where:

Cv=at 0% GSV opening

k=cp/cv is the one of the leanest gas from job CSO

Sg is the one of the leanest gas from fuel job CSO

4^(th) STEP—GCV choking verification

If,

  (?) > 1.23, ?indicates text missing or illegible when filed

then the unit is able to transfer into PREMIX aside from EXT-LL mode.

The logic flows depicted in the figures do not require the particularorder shown, or sequential order, to achieve desirable results. Inaddition, other steps may be provided, or steps may be eliminated, fromthe described flows, and other components may be added to, or removedfrom, the described systems. Accordingly, other embodiments are withinthe scope of the following claims.

It will be appreciated that the above embodiments that have beendescribed in particular detail are merely example or possibleembodiments, and that there are many other combinations, additions, oralternatives that may be included.

Also, the particular naming of the components, capitalization of terms,the attributes, data structures, or any other programming or structuralaspect is not mandatory or significant, and the mechanisms thatimplement the invention or its features may have different names,formats, or protocols. Further, the system may be implemented via acombination of hardware and software, as described, or entirely inhardware elements. Also, the particular division of functionalitybetween the various system components described herein is merely oneexample, and not mandatory; functions performed by a single systemcomponent may instead be performed by multiple components, and functionsperformed by multiple components may instead performed by a singlecomponent.

Some portions of above description present features in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations may be used by thoseskilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. These operations,while described functionally or logically, are understood to beimplemented by computer programs. Furthermore, it has also provenconvenient at times, to refer to these arrangements of operations asmodules or by functional names, without loss of generality.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or “displaying” or “providing” or thelike, refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem memories or registers or other such information storage,transmission or display devices.

While the disclosure has been described in terms of various specificembodiments, it will be recognized that the disclosure can be practicedwith modification within the spirit and scope of the claims.

The term processor, as used herein, refers to central processing units,microprocessors, microcontrollers, reduced instruction set circuits(RISC), application specific integrated circuits (ASIC), logic circuits,and any other circuit or processor capable of executing the functionsdescribed herein.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution byprocessor 205, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexemplary only, and are thus not limiting as to the types of memoryusable for storage of a computer program.

As will be appreciated based on the foregoing specification, theabove-described embodiments of the disclosure may be implemented usingcomputer programming or engineering techniques including computersoftware, firmware, hardware or any combination or subset thereof,wherein the technical effect includes (a) storing a plurality rule setsin the memory device, the rule sets relative to the operation of the gasturbine, the rule sets including at least one rule expressed as arelational expression of a real-time data output relative to a real-timedata input, the relational expression being specific to at least one ofa temperature spread of an exhaust flow of the gas turbine, a swirlangle of the exhaust flow, a health of a plurality of flame detectors ofthe gas turbine, and a transfer of the gas turbine from a first mode ofoperation to a second lower NOX mode of operation, (b) receivingreal-time and historical data inputs from a condition monitoring systemassociated with the gas turbine, the data inputs relating to parametersaffecting at least one of the temperature spread of the exhaust flow ofthe gas turbine, the swirl angle of the exhaust flow, the health of theplurality of flame detectors of the gas turbine, and the transfer of thegas turbine from the first mode of operation to the second lower NOXmode of operation, (c) determining a fuel gas line pressure drop usingthe received data, (d) comparing the determined pressure drop to apredetermined threshold range; and (e) recommending to an operator ofthe gas turbine to transfer the mode of operation of the gas turbinefrom the first mode to the second mode without reducing a load of thegas turbine if the determined pressure drop meets the predeterminedthreshold range. Any such resulting program, having computer-readablecode means, may be embodied or provided within one or morecomputer-readable media, thereby making a computer program product,i.e., an article of manufacture, according to the discussed embodimentsof the disclosure. The computer readable media may be, for example, butis not limited to, a fixed (hard) drive, diskette, optical disk,magnetic tape, semiconductor memory such as read-only memory (ROM),and/or any transmitting/receiving medium such as the Internet or othercommunication network or link. The article of manufacture containing thecomputer code may be made and/or used by executing the code directlyfrom one medium, by copying the code from one medium to another medium,or by transmitting the code over a network.

Many of the functional units described in this specification have beenlabeled as modules, in order to more particularly emphasize theirimplementation independence. For example, a module may be implemented asa hardware circuit comprising custom very large scale integration(“VLSI”) circuits or gate arrays, off-the-shelf semiconductors such aslogic chips, transistors, or other discrete components. A module mayalso be implemented in programmable hardware devices such as fieldprogrammable gate arrays (FPGAs), programmable array logic, programmablelogic devices (PLDs) or the like.

Modules may also be implemented in software for execution by varioustypes of processors. An identified module of executable code may, forinstance, comprise one or more physical or logical blocks of computerinstructions, which may, for instance, be organized as an object,procedure, or function. Nevertheless, the executables of an identifiedmodule need not be physically located together, but may comprisedisparate instructions stored in different locations which, when joinedlogically together, comprise the module and achieve the stated purposefor the module.

A module of executable code may be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different programs, and across several memory devices.Similarly, operational data may be identified and illustrated hereinwithin modules, and may be embodied in any suitable form and organizedwithin any suitable type of data structure. The operational data may becollected as a single data set, or may be distributed over differentlocations including over different storage devices, and may exist, atleast partially, merely as electronic signals on a system or network.

The above-described embodiments of a method and monitoring anddiagnostic system for a gas turbine that includes a rule module providesa cost-effective and reliable means for providing meaningful operationalrecommendations and troubleshooting actions. Moreover, the system ismore accurate and less prone to false alarms. More specifically, themethods and systems described herein can predict component failure at amuch earlier stage than known systems to facilitate significantlyreducing outage time and preventing trips. In addition, theabove-described methods and systems facilitate predicting anomalies atan early stage enabling site personnel to prepare and plan for ashutdown of the equipment. As a result, the methods and systemsdescribed herein facilitate operating gas turbines and other equipmentin a cost-effective and reliable manner.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe disclosure is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

What is claimed is:
 1. A computer-implemented method for monitoring anddiagnosing combustion anomalies in an operation of a gas turbine, themethod implemented using a computer device coupled to a user interfaceand a memory device, the method comprising: storing a plurality rulesets in a memory device, the rule sets relative to the operation of thegas turbine, the rule sets comprising at least one rule expressed as arelational expression of a real-time data output relative to a real-timedata input, the relational expression being specific to at least one ofa temperature spread of an exhaust flow of the gas turbine, a swirlangle of the exhaust flow, a health of a plurality of secondary flamedetectors of the gas turbine, and a transfer of the gas turbine from afirst mode of operation to a second lower NO_(X) mode of operation;receiving real-time and historical data inputs from a conditionmonitoring system associated with the gas turbine, the data inputsrelating to parameters affecting at least one of the temperature spreadof the exhaust flow of the gas turbine, the swirl angle of the exhaustflow, the health of the plurality of flame detectors of the gas turbine,and the transfer of the gas turbine from the first mode of operation tothe second lower NO_(X) mode of operation; determining a fuel gas linepressure drop using the received data; comparing the determined pressuredrop to a predetermined threshold range; and recommending to an operatorof the gas turbine to transfer the mode of operation of the gas turbinefrom the first mode to the second mode without reducing a load of thegas turbine if the determined pressure drop meets the predeterminedthreshold range.
 2. The method of claim 1, wherein storing a pluralityrule sets comprises storing a gas turbine transfer rule set wherein thefirst mode of operation is an Extended Lean-Lean (EXT-LL) mode and thesecond lower NO_(X) mode of operation is a Premix mode.
 3. The method ofclaim 1, further comprising: receiving an analog signal output of atleast some of the plurality of flame detectors; statistically analyzingeach analog signal output to identify a noise component of the signaland a variation of the signal; generating a health count metric of thesignals to define a plurality of thresholds based on the analysis;comparing a current analog signal output to respective threshold; andoutputting a recommendation to at least one of replace one of theplurality of flame detectors, tune one of the plurality of flamedetectors, check the operation of one of the plurality of flamedetectors, and clean a lens of one of the plurality of flame detectors.4. The method of claim 1, further comprising: determining a swirl angleof a flow of gas turbine exhaust; determining a faulty combustor usingthe determined swirl angle; and outputting the determined faultycombustor to an operator.
 5. The method of claim 4, wherein determininga swirl angle comprises: receiving a plurality of temperature outputsfrom one or more temperature sensors associated with the flow of gasturbine exhaust; and determining a temperature spread of the flow of gasturbine exhaust using the received plurality of temperature outputs. 6.The method of claim 5, further comprising correlating the determinedtemperature spread to a predetermined allowable temperature spread todetermine an identity of a source combustor of the temperature spread.7. The method of claim 5, wherein determining a temperature spread ofthe flow of gas turbine exhaust comprises determining a temperaturespread of the flow of gas turbine exhaust at an exhaust diffuser of thegas turbine.
 8. The method of claim 5, wherein determining a temperaturespread of the flow of gas turbine exhaust comprises determining atemperature spread of the flow of gas turbine exhaust as a function ofcombustion mode and load.
 9. The method of claim 5, wherein the gasturbine is capable of operating in a plurality of different combustionmodes, the method further comprising determining a temperature spreadthreshold for each different combustion mode.
 10. The method of claim 9,further comprising setting a temperature spread threshold to a valuecorresponding to a combustion mode being entered at least one ofcoincident to the transition into the combustion mode being entered andprior to the transition into the combustion mode being entered.
 11. Asystem for monitoring and diagnosing combustion anomalies in anoperation of a gas turbine, the system comprising: a memory device; acondition monitoring system associated with the gas turbine; an userinterface; and a process configured to: store a plurality rule sets inthe memory device, the rule sets relative to the operation of the gasturbine, the rule sets comprising at least one rule expressed as arelational expression of a real-time data output relative to a real-timedata input, the relational expression being specific to at least one ofa temperature spread of an exhaust flow of the gas turbine, a swirlangle of the exhaust flow, a health of a plurality of secondary flamedetectors of the gas turbine, and a transfer of the gas turbine from afirst mode of operation to a second lower NO_(X) mode of operation,receive real-time and historical data inputs from the conditionmonitoring system, the data inputs relating to parameters affecting atleast one of the temperature spread of the exhaust flow of the gasturbine, the swirl angle of the exhaust flow, the health of theplurality of flame detectors of the gas turbine, and the transfer of thegas turbine from the first mode of operation to the second lower NO_(X)mode of operation, determine a fuel gas line pressure drop using thereceived data, compare the determined pressure drop to a predeterminedthreshold range, and recommend through the user interface to an operatorof the gas turbine to transfer the mode of operation of the gas turbinefrom the first mode to the second mode without reducing a load of thegas turbine if the determined pressure drop meets the predeterminedthreshold range.
 12. The system of claim 11, wherein storing a pluralityrule sets comprises storing a gas turbine transfer rule set wherein thefirst mode of operation is an Extended Lean-Lean (EXT-LL) mode and thesecond lower NO_(X) mode of operation is a Premix mode.
 13. The systemof claim 11, wherein the processor is further configured to: receive ananalog signal output of at least some of the plurality of flamedetectors, statistically analyze each analog signal output to identify anoise component of the signal and a variation of the signal, generate ahealth count metric of the signals to define a plurality of thresholdsbased on the analysis, compare a current analog signal output torespective threshold, and output a recommendation to at least one ofreplace one of the plurality of flame detectors, tune one of theplurality of flame detectors, check the operation of one of theplurality of flame detectors, and clean a lens of one of the pluralityof flame detectors.
 14. The system of claim 11, wherein the processor isfurther configured to: determine a swirl angle of a flow of gas turbineexhaust, determine a faulty combustor using the determined swirl angle,and output the determined faulty combustor to an operator.
 15. Thesystem of claim 14, wherein determining a swirl angle comprises:receiving a plurality of temperature outputs from one or moretemperature sensors associated with the flow of gas turbine exhaust; anddetermining a temperature spread of the flow of gas turbine exhaustusing the received plurality of temperature outputs.
 16. The system ofclaim 15, wherein the processor is further configured to correlate thedetermined temperature spread to a predetermined allowable temperaturespread to determine an identity of a source combustor of the temperaturespread.
 17. The system of claim 15, wherein determining a temperaturespread of the flow of gas turbine exhaust comprises determining atemperature spread of the flow of gas turbine exhaust at an exhaustdiffuser of the gas turbine.
 18. The system of claim 15, whereindetermining a temperature spread of the flow of gas turbine exhaustcomprises determining a temperature spread of the flow of gas turbineexhaust as a function of combustion mode and load.
 19. The system ofclaim 15, wherein the gas turbine is capable of operating in a pluralityof different combustion modes, and the processor is further configuredto determine a temperature spread threshold for each differentcombustion mode.
 20. The system of claim 19, wherein the processor isfurther configured to set a temperature spread threshold to a valuecorresponding to a combustion mode being entered at least one ofcoincident to the transition into the combustion mode being entered andprior to the transition into the combustion mode being entered.